DOI QR코드

DOI QR Code

Impacts of Low User and Project Management Risk on IT Project of Public Sector: The Moderating Effect of User Knowledge

사용자 및 프로젝트 관리 위험 감소가 공공부문 IT 프로젝트 성과에 미치는 영향 : 사용자 지식의 조절효과

  • Lee, Sooin (School of Business Administration, Kyungpook National University) ;
  • Kim, Sanghyun (School of Business Administration, Kyungpook National University)
  • 이수인 (경북대학교 경영학부 일반대학원) ;
  • 김상현 (경북대학교 경영학부)
  • Received : 2020.03.31
  • Accepted : 2020.06.20
  • Published : 2020.06.28

Abstract

We tried to do research on main views(risk and knowledge-based) and propose user knowledge as a factor to be managed in order to strengthen the performance. This study empirically analyzed the user-related and project management risk that affect the IT project performance, and verified user knowledge as a moderating variable. The survey was conducted for respondents who have experience on IT Project of public sector and data were analyzed by Smart PLS 3.0. The results show that low user-related and project management risks have a positive effect on performance. In addition, user knowledge has been shown to enhance the impact of two risks on performance. These findings are meaningful in that they emphasize the importance of user knowledge in public-sector IT projects, as well as the repeated verification of integrated research results.

IT 프로젝트 성과를 위한 두 가지(위험 기반, 지식 기반) 주요 관점을 통합적으로 연구하고자 하였으며, 민간부문보다 상대적으로 제약이 많은 공공부문 IT프로젝트가 성과를 확대하기 위해 관리할 요인으로 사용자 지식을 제안하고자 하였다. 이에 본 연구는 성과에 미치는 2가지 유형의 위험(사용자 관련 위험과 프로젝트 관리 위험) 요인을 실증분석하였으며, 사용자 지식을 조절효과로 검증하였다. 공공기관 IT 프로젝트 참여 경험이 있는 응답자를 대상으로, 132부의 데이터를 수집하여 Smart PLS 3.0으로 분석하였다. 연구결과, 낮은 사용자 관련 위험과 낮은 프로젝트 관리 위험은 IT 프로젝트 성과에 정(+)의 영향을 미치는 것으로 나타났다. 또한, 사용자 지식은 사용자 관련 위험과 낮은 프로젝트 관리 위험이 IT 프로젝트 성과에 미치는 영향을 강화하는 것으로 나타났다. 이러한 연구결과는 통합적 연구결과의 반복적인 검증이자, 공공부문 IT 프로젝트에서 갖는 사용자 지식의 중요성을 강조한다.

Keywords

References

  1. The Standish Group. (2015). The 2015 CHAOS Report. http://infoq.com/articles/standish-chaos-2015
  2. PMI. (2017) "Success Rates Rise : Transforming the high cost of low performance." In PMI's Pulse of the Profession 2017, Project Management Institute.
  3. S. Liu, (2016). How the user liaison's understanding of development processes moderates the effects of user-related and project management risks on IT project performance. Information & Management, 53(1), 122-134. https://doi.org/10.1016/j.im.2015.09.004
  4. H. Taylor, E. Artman, & J. P. Woelfer, (2012). Information technology project risk management: bridging the gap between research and practice. Journal of Information Technology, 27(1), 17-34. https://doi.org/10.1057/jit.2011.29
  5. J. Y. Kim. J. Y. Jang & G. J. Choi. 2018. The Effect of Knowledge Complementarity and PMO Implementation System on Performance of IT Project. Management and Information Systems Review, 37(4): 141-156. https://doi.org/10.29214/damis.2018.37.4.009
  6. B. H. Reich, A. Gemino & A. Sauer. (2014). How knowledge management impacts performance in projects: An empirical study. International Journal of Project Management, 32, 590-602. https://doi.org/10.1016/j.ijproman.2013.09.004
  7. J. J. Jiang, G. Klein, H. Chen & L. Lin, (2002). Reducing user-related risks during and prior to system development. International journal of project management, 20(7), 507-515. https://doi.org/10.1016/S0263-7863(01)00049-7
  8. L. Wallace. M. Keil & A. Rai. (2004). How software project risk affects project performance: An investigation of the dimensions of risk and an exploratory model. Decision sciences, 35(2), 289-321. https://doi.org/10.1111/j.00117315.2004.02059.x
  9. S. Liu & L. Wang. (2014). Understanding the impact of risks on performance in internal and outsourced information technology projects: The role of strategic importance. International Journal of Project Management, 32(8), 1494-1510. https://doi.org/10.1016/j.ijproman.2014.01.012
  10. J. H. Ahn & S. G. Chang. (2004). Assessing the contribution of knowledge to business performance: the KP3 methodology. Decision Support Systems, 36, 403-416. https://doi.org/10.1016/S0167-9236(03)00029-0
  11. T. Suh. M.. Bae. H. Zhao. S. H. Kim & M. J. Arnold. (2010). A multi-level investigation of international marketing projects: The roles of experiential knowledge and creativity on performance. Industrial Marketing Management, 39(2), 211-220. https://doi.org/10.1016/j.indmarman.2008.08.007
  12. PMI STANDARDS COMMITTEE. (2012). A Guide to the Project Management Body of Knowledge, Project Management Institute.
  13. S. H. Oh. & S. C. Kim. (2015). A Study of the Effects of Project Portfolio Management on the Competitive Advantage with Dynamic Capability Theory in the Defense Industry. Global Business Administration Review, 12: 579-604.
  14. D. Baccarini. (1999). The logical framework method for defining project success. Project management journal, 30(4), 25-32. https://doi.org/10.1177/875697289903000405
  15. S. H. Ji. K. I. Sohn & S. C. Kim. 2012. Analyzing the Impacts of Project Management Knowledge on the Project Performance: Cases of System Integration Project. Project Management Review, 2(2), 17-33.
  16. R. Atkinson, (1999). Project management: cost, time and quality, two best guesses and a phenomenon, its time to accept other success criteria. International journal of project management, 17(6), 337-342. https://doi.org/10.1016/S0263-7863(98)00069-6
  17. L. Geoghegan, & V. Dulewicz, (2008). Do project managers' leadership competencies contribute to project success? Project Management Journal, 39(4), 58-67. https://doi.org/10.1002/pmj.20084
  18. J. K. Pinto & D. P. Slevin, (1988). "Critical Success Factors in Effective Project implementation." In Project management handbook, 479.
  19. N. Agarwal & U. Rathod, (2006). Defining 'success' for software projects: An exploratory revelation. International journal of project management, 24(4), 358-370. https://doi.org/10.1016/j.ijproman.2005.11.009
  20. D. Dvir & T. Lechler, (2004). Plans are nothing, changing plans is everything: the impact of changes on project success. Research policy, 33(1), 1-15. https://doi.org/10.1016/j.respol.2003.04.001
  21. B. Y. Lee & S. G. Lee. 2015. A Study on the Importance of the Impact Factors on the Performance of IT Project. Journal of KIIT, 13(1): 127-138.
  22. S. B. Jang & G. Y. Kwahk. 2011. The Effects of IT Project Risk Management Factors on Project Performance. Korean Management Science Review, 28(2): 31-51.
  23. F. W. McFarlan. (1981) Portfolio Approach to Information Systems, Harvard Business Review, 59(5), 142-150.
  24. R. Schmidt, K. Lyytinen, M. Keil & P. Cule, (2001). Identifying software project risks: An international Delphi study. Journal of management information systems, 17(4), 5-36. https://doi.org/10.1080/07421222.2001.11045662
  25. P. L. Bannerman, (2008). Risk and risk management in software projects: A reassessment. Journal of Systems and Software, 81(12), 2118-2133. https://doi.org/10.1016/j.jss.2008.03.059
  26. K. De Bakker, A. Boonstra, & H. Wortmann. (2010). Does risk management contribute to IT project success? A meta-analysis of empirical evidence. International Journal of Project Management, 28(5), 493-503. https://doi.org/10.1016/j.ijproman.2009.07.002
  27. M. Keil. P.E. Cule, K. Lyytinen & R. C. Schmidt. (1998). A framework for Identifying Software Project Risks, Communication of the ACM, 41(11), 76-83. https://doi.org/10.1145/287831.287843
  28. P. Tait & I. Vessey. (1988). The effect of user involvement on systems success: a contingency approach. MIS Quarterly, 12(1), 91-107. https://doi.org/10.2307/248809
  29. H. Barki, & J. Hartwick. (1989). Rethinking the concept of user involvement. MIS quarterly, 53-63.
  30. Y. W. Hung. S. C. Hsu. Z. Y. Su. & H. H. Huang. (2014). Countering user risk in information system development projects. International Journal of Information Management, 34(4), 533-545. https://doi.org/10.1016/j.ijinfomgt.2014.02.003
  31. S. Liu. J. Zhang. M.. Keil. & T. Chen. (2010). Comparing senior executive and project manager perceptions of IT project risk: a Chinese Delphi study. Information Systems Journal, 20(4), 319-355. https://doi.org/10.1111/j.1365-2575.2009.00333.x
  32. H. Akbar & S. Mandurah. (2014). Project-conceptualisation in technological innovations: A knowledge-based perspective. International Journal of Project Management, 32(5), 759-772. https://doi.org/10.1016/j.ijproman.2013.10.002
  33. S. H. Lee & H. G. Lee. (2007). A Study on Effects of Knowledge Transfer Processes on IS Development Project Performance. Journal of Knowledge Studies, 5(1): 97-138.
  34. J. L. Spears & H. Barki. (2010). User participation in information systems security risk management. MIS quarterly, 503-522.
  35. J. K. Bae. J. H, Kim & S. Y. Kim. (2008). An Exploratory Study on the Project Performance by PMO Capability. Asia Pacific Journal of Information Systems, 18(1): 53-77.
  36. J. He, B. S. Butler & W. R. King, (2007). Team cognition: Development and evolution in software project teams. Journal of Management Information Systems, 24(2), 261-292. https://doi.org/10.2753/MIS0742-1222240210
  37. L. J. Kirsch. V. Sambamurthy. D. G. Ko & R. L. Purvis. (2002). Controlling information systems development projects: The view from the client. Management science, 48(4), 484-498. https://doi.org/10.1287/mnsc.48.4.484.204
  38. D. Tesch. M. G. Sobol. G. Klein & J. J. Jiang. (2009). User and developer common knowledge: Effect on the success of information system development projects. International Journal of Project Management, 27(7), 657-664. https://doi.org/10.1016/j.ijproman.2009.01.002
  39. M. N. Mirza. Z. Pourzolfaghar & M. Shahnazari. (2013). Significance of scope in project success. Procedia Technology, 9(1).
  40. A. Tiwana. & M.. Keil. (2007). Does peripheral knowledge complement control? An empirical test in technology outsourcing alliances. Strategic Management Journal, 28(6), 623-634. https://doi.org/10.1002/smj.623
  41. S. Rustagi. W. R. King & L. J. Kirsch. (2008). Predictors of formal control usage in IT outsourcing partnerships. Information systems research, 19(2), 126-143. https://doi.org/10.1287/isre.1080.0169
  42. B. Wilson & J. Henseler. (2007). Modeling reflective higher-order constructs using three approaches with PLS path modeling: A Monte Carlo comparison. in Thyne, M. and Deans, K.R. (Eds), Conference Proceedings ANZMAC 2007, ANZMAC, Dunedin, pp. 791-800.
  43. C. Fornell & D. F. Larcker. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of Marketing Reseach, 18(3), 382-388. https://doi.org/10.1177/002224378101800313
  44. E. G. Carmines & R. A. Zeller. (1979). Reliability and validity assessment, Los Angels, CA: Sege Publications.
  45. J. H. Ra. (2017). An Exploratory Study on the Selection of Mandatory Subjects for Information Strategic Planning - Focused on Public Sector. The Journal of Digital Policy & Management, 15(4), 35-42.
  46. D. H. Han & T. K. Kang. (2004). The Affecting Factors effect on the Performance of Knowledge Management Systems in Public Sector: Focused on Korea Local Governments. The Journal of Digital Policy & Management, 2(1), 63-73.
  47. PMI. (2017). PMBOK guide, Newtown Square, PA: Project Management Institute.
  48. J. M. Jun. & S. G. Yi. (2013). Adjustment effect of the suitability factors of strategy between Information Technology Outsourcing(ITO)'s influence and outcome factors in Government offices. The Journal of Digital Policy & Management, 11(12), 29-40.